Clustered Monotone Transforms for Rating Factorization (CMTRF) google
Exploiting low-rank structure of the user-item rating matrix has been the crux of many recommendation engines. However, existing recommendation engines force raters with heterogeneous behavior profiles to map their intrinsic rating scales to a common rating scale (e.g. 1-5). This non-linear transformation of the rating scale shatters the low-rank structure of the rating matrix, therefore resulting in a poor fit and consequentially, poor recommendations. In this paper, we propose Clustered Monotone Transforms for Rating Factorization (CMTRF), a novel approach to perform regression up to unknown monotonic transforms over unknown population segments. Essentially, for recommendation systems, the technique searches for monotonic transformations of the rating scales resulting in a better fit. This is combined with an underlying matrix factorization regression model that couples the user-wise ratings to exploit shared low dimensional structure. The rating scale transformations can be generated for each user, for a cluster of users, or for all the users at once, forming the basis of three simple and efficient algorithms proposed in this paper, all of which alternate between transformation of the rating scales and matrix factorization regression. Despite the non-convexity, CMTRF is theoretically shown to recover a unique solution under mild conditions. Experimental results on two synthetic and seven real-world datasets show that CMTRF outperforms other state-of-the-art baselines. …

Enterprise Information Flow (EIF) google
What is Enterprise Information Flow? The concept is closely connected to its neighboring disciplines: Information Flow, Data Lineage Analysis and Metadata Management. But it’s not the same. This new buzzword is only beginning to be recognized, so let’s get a head start. Information Flow focuses on information processing when it comes to security, throughput optimization and transporters; Data Lineage Analysis studies the way data is transferred between systems; and Metadata Management is all about metadata structure and purpose. Why do we need a new concept then?
· Big data is getting really big. From internal company systems, social networks and external data from partners to automatically collected data – a huge amount of information needs to be properly dealt with. The high volume of data is of course connected to the high volume of contributing sources: dozens of online channels, the previously mentioned social networks, portable devices, blogs, news and video content. And every source needs to be correctly described, attributed and integrated into the company’s Enterprise Information Flow.
· Systems are getting more and more complicated. EIF needs to be ready not only for big data coming from a wide variety of channels, but also for the many different ways data is transformed and processed inside the system. Old school transformation methods like ETL and SQL scripts are easy and usually well-accounted for, but cracks start to show when it comes to the semantic analysis of non-structured data, Google’s search algorithms, Facebook’s preferential algorithms, automated quality assurance scripts or artificial intelligence methods used for predictive analysis. When it comes to transformations, it’s critical to know how security and other specific attributes change. Another key point is deciding if the information is created or just transformed.
· New routes between systems. The number of different ways to transfer data between systems is rapidly growing. Classic ETL and extract transfers are joined by more complicated systems based on SOA, PBM and ESB. It’s also necessary to be ready for new approaches like Data Federation and Logical Data Warehouse, where data saving is not persistent.
· Different data types. It’s not about relational data or text anymore. You need to be ready for NoSQL databases, hyperlinks, video, graphics, xml, semi-structured data and other types of information. In complicated environments like these, current solutions fail. New approaches need to be more complex, as the new systems are. It’s necessary to follow data not only on a physical level, but also through more layers of logical abstraction.
Let’s sum it up into two main angles of Enterprise Information Flow:
1) New information necessary for decision making appears. Where does it come from? When was it created? Who’s responsible for its quality?
2) Who uses my information and how?
Those two sets of questions are vital to Enterprise Information Flow which is a standard part of Enterprise Information Management. Any organization who takes its data seriously is searching for answers anyway, but EIF can provide a more comprehensive overview and merge existing solutions from currently separated fields into one complex policy. A complex solution is precisely what you need, when you’re dealing with complex systems. …

Faithfulness Condition google
The faithfulness condition states that also all independences among of the two possible explanations for spurious causalities of type I, we accept only the second with an additional latent confounding variable. Consequently, detection of spurious causalities of type I allows identifying spurious causalities of type II without knowing the confounding variable. the variables are implied by the causal structure. In particular, this rules out that two or more causal links cancel each other out due to a particular choice of the parameters. This means that, …